The effect of periodic phenomena, registered as signals, is usually studied using common signal analysis tools such as Fourier analysis. A given signal, as long as it manifests some periodicity, can be thought of as being composed of a series of single sinusoidal components or, more commonly named, tones. Frequency domain characterization of signals amounts to the identification of individual constituting components of a given signal and their corresponding values of amplitude and phase. In the case of nonstationary signals, all characteristics of the constituting components, including frequency, may vary over time. The main shortcoming of Fourier-based methods is their inherent fixed-frequency assumption which limits their applicability to the majority of real signals, the frequency characteristics of which may vary with time. There have been numerous attempts to devise adaptation mechanisms to be incorporated into signal analysis tools to render them useful for analysis of quasi-periodic signals. Linear adaptive filtering is an example of such methods reported so far with its success and shortcomings.
Aside from the time-varying quality of real signals, very often signals are buried under noise and disturbances and may thus be severely distorted. Often, a useful signal analysis tool loses its efficiency when it is applied to signals affected by noise and disturbances. Thus, it is often necessary to recover the signal itself out of the background noise, especially under nonstationary conditions. Extraction of the signal itself, and not just its characteristics, is of particular interest in applications where synchronization matters; i.e. where the total phase information of a signal is important. In such cases, a single sinusoidal component of a given signal, or the totality of a number of such sinusoids, is to be extracted, or equivalently a desirable noise-free synchronized signal is to be synthesized. Synthesization of signals synchronous with a given reference signal finds applications in more areas than those dealing with extraction of signals out of noise and may very well include those applications in which phase-locked loop (PLL) circuits and systems are employed.
Considering the inadequacy of the performance of the available standard signal analysis tools, such as Fourier-based techniques, adaptive filters and PLLs, in extracting sinusoids of time-varying nature buried under noise in a unified way, it is not difficult to explain the existing diversity of the methods, each designed to tackle a specific type of problem.
Signal processing techniques and systems, and in particular methods of extraction of a desired signal within a noisy environment find significant application in audiometry, i.e., the measuring of how well someone can hear. Thus improvements in techniques, systems and methods for the extraction of a signal from a noisy environment can lead to improvements in audiometry.
Conventional audiometry is performed by having a subject respond to acoustic stimuli by pressing a button, saying “yes”, or repeating words that may be presented in the stimulus. These tests are subjective in nature. Audiometry allows an audiologist to determine the auditory threshold of the subject, which is defined as the lowest intensity at which a sound can be heard. The audiologist evaluates the auditory threshold of a subject by using a stimulus that most commonly consists of a pure tone. The stimulus is presented via earphones, headphones, free field speakers or bone conduction transducers. The results are presented as an audiogram which shows auditory thresholds for tones of different frequencies. The audiogram is helpful for diagnosing the type of hearing loss a subject may have. The audiogram can also be used to fit a hearing aid and adjust the level of amplification of the hearing aid for subjects who require hearing aids.
Conventional audiometry cannot be performed if the subject is an infant, young child or cognitively impaired adult. In these cases; objective tests of hearing are necessary in which the subject does not have to make a conscious response. Objective audiometry is essential for detecting hearing impairment in infants or elderly patients as well as for evaluating functional hearing losses. Furthermore, few objective tests have been developed for supra-threshold tests of speech, frequency, or intensity discrimination.
One form of objective audiometry uses auditory evoked potentials. Auditory evoked potential testing consists of presenting the subject with an acoustic stimulus and simultaneously or concurrently sensing (i.e. recording) potentials from the subject. The sensed potentials are the subject's electroencephalogram (EEG) which contain the subject's response to the stimulus if the subject's auditory system has processed the stimulus. These potentials are analyzed to determine whether they contain a response to the acoustic stimulus or not. Auditory evoked potentials have been used to determine auditory thresholds and hearing at specific frequencies.
One particular class of auditory evoked potentials is steady-state auditory evoked potentials (SSAEPs). The stimulus for the SSAEP consists of a carrier signal, which is usually a sinusoid, that is amplitude modulated by a modulation signal which is also usually a sinusoid. The SSAEP stimulus is presented to the subject while simultaneously recording the subject's EEG. If the auditory system of the subject responded to the SSAEP stimulus, then a corresponding steady-state sinusoidal signal should exist in the recorded EEG. The signal should have a frequency that is the same as the frequency of the modulation signal (i.e. modulation frequency). The presence of such a corresponding signal in the EEG is indicative of a response to the SSAEP stimulus. Alternatively, the phase of the carrier signal may be frequency modulated instead of or in addition to amplitude modulation to create the SSAEP stimulus. A system and method of SSAEP audiometry is described in U.S. Pat. No. 6,602,202, the disclosure of which is hereby expressly incorporated by reference.
However, objective audiometry employing SSAEP testing is time-consuming because the amplitude of the SSAEP response is quite small compared to the background noise, which is the subject's on going brain activity (i.e. EEG) while the test is being conducted. The SSAEP response thus has a small signal-to-noise ratio (SNR) which makes it difficult to detect the SSAEP response in a short time period. Thus, improvements in techniques, systems and methods of detecting the SSAEP response may lead to improvements in SSAEP audiometry.
Another form of objective audiometry is otoacoustic emission (OAE) audiometry. One form of OAE is distortion product otoacoustic emission audiometry (DPOAE). Distortion product otoacoustic emissions (DPOAEs) are very low level stimulated acoustic responses to two pure tones presented to the ear canal. DPOAE measurement provides an objective non-invasive measure of peripheral auditory function and is used for hearing assessment. DPOAE screening is becoming a standard clinical practice to predict potential sensorineural hearing loss especially in newborns.
DPOAEs have been recognized for a number years. But DPOAE measurement remains difficult because of the challenging nature of the signal processing task. In this type of otoacoustic test, two pure tones with frequencies f1 and f2 are presented to the cochlea. For best results, f2 is usually chosen as 1.2f1. Due to the non-linearity of the ear, a very low level of distortion product of frequency 2f1−f2 is generated in normal ears. The level of such DPOAE signal is a measure of functionality of the ear. Estimation of such a weak signal buried under two strong artifacts in a potentially noisy background is a challenging signal processing problem.
Conventionally, fast Fourier transform (FFT) is used as the main signal processing tool to estimate the level of DPOAE signals. Application of FFT in this problem has a number of shortcomings among which long measurement time is most pronounced. Such long measurement time is usually required for acquisition of more data which, when averaged, reduce the overall background noise effect. Unreliability of the measurements is another problem of FFT-based methods and is a direct result of the sensitivity of the FFT-based methods to the background noise. In addition to the need to increase the measurement time, the tests are usually required to be conducted in low noise environments such as sound-proof booths.
In an attempt to devise high performance DPOAE estimation techniques, linear adaptive signal processing techniques have been employed. Such techniques generally offer better performance in terms of measurement time, which may be interpreted as higher noise immunity of adaptive techniques compared to FFT; however, the need for sound-proof examination rooms is not obviated with such techniques.